Invasive fungal infections (IFIs) have become prominent global health threats, escalating the burden on public health systems. The increasing occurrence of invasive fungal infections is due primarily to the extensive application of chemotherapy, immunosuppressive therapies, and broad-spectrum antifungal agents. At present, therapeutic practices utilize multiple categories of antifungal agents, such as azoles, polyenes, echinocandins, and pyrimidine analogs. Nevertheless, the clinical effectiveness of these treatments is progressively weakened by the emergence of drug resistance, thereby substantially restricting their therapeutic utility. Consequently, there is an imperative need to expedite the discovery of novel antifungal agents. This review seeks to present an exhaustive synthesis of novel antifungal drugs and candidate agents that are either under current clinical investigation or anticipated to progress into clinical evaluation. These emerging compounds exhibit unique benefits concerning their modes of action, antimicrobial spectra, and pharmacokinetic characteristics, potentially leading to improved therapeutic outcomes relative to conventional antifungal regimens. It is anticipated that these novel therapeutic agents will furnish innovative treatment modalities and enhance clinical outcomes in managing invasive fungal infections.

Objective To construct a multivariable prediction model for assessing the risk of cardiopulmonary complication after thoracoscopic lobectomy in patients with stage Ⅰ-Ⅱ non-small cell lung cancer(NSCLC).Methods Clinical data of 600 patients with stage Ⅰ-Ⅱ NSCLC who underwent thoracoscopic lobectomy were retrospectively analyzed.Patients were divided into the complication group(84 cases)and the non-complication group(516 cases)based on the occurrence of postoperative complication within 7 days,including atelectasis,pulmonary embolism,respiratory failure,chylothorax,massive pleural effusion,hypoxemia and atrial fibrillation.Demographic characteristics,preoperative pulmonary function assessment,pathological features and perioperative indicators were collected.Multivariate Logistic regression analysis was used to identify independent risk factors influencing postoperative cardiopulmonary complication in patients and construct a predictive model.Internal validation was performed using the Bootstrap resampling method(1 000 iterations)to evaluate the discrimination,calibration and clinical decision-making value of the model.Results Multivariate Logistic regression analysis identified the following independent risk factors of postoperative cardiopulmonary complication,including age(OR=1.832,95%CI:1.537-2.183),history of chronic obstructive pulmonary disease(COPD)(OR=6.782,95%CI:2.685-17.130),Karnofsky performance status(KPS)score(OR=0.926,95%CI:0.888-0.965),the percentage of forced expiratory volume in the first second to the predictive value(FEV1%pred)(OR=0.906,95%CI:0.845-0.972),the percentage of diffusing capacity for carbon monoxide to the expected value(DLCO%pred)(OR=0.901,95%CI:0.832-0.975),intraoperative blood loss(OR=1.025,95%CI:1.014-1.036)and one-lung ventilation time(OR=1.057,95%CI:1.034-1.080).The area under the curve(AUC)of the combined diagnosis was 0.977(95%CI:0.965-0.989),with 96.4%sensitivity and 87.6%specificity.The Hosmer-Lemeshow test indicated excellent calibration(χ2=1.285,P=0.994).Decision curve analysis demonstrated significant clinical net benefit when the risk threshold probability ranged between 20%and 98%.Conclusion The multivariable prediction model for cardiopulmonary complication after thoracoscopic lobectomy in stage Ⅰ-Ⅱ NSCLC patients exhibits strong predictive performance.

Objective:To investigate the risk factors associated with the development of venous thromboembolism with abdominal infection following pancreatic cancer surgery.Methods:A retrospective analysis was conducted on the clinical data of 70 patients with pancreatic cancer who underwent pancreaticoduodenectomy at the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Air Force Medical University from March 2020 to August 2024, of which 31 were male and 39 were female, aged (58.5±6.8) years. Two groups were divided into two groups according to the occurrence of venous thromboembolism with abdominal infection in the postoperative period: the complication group ( n=28) and the non-complication group ( n=42). The general data, laboratory indexes and surgery-related indexes of the two groups were recorded, and logistic regression model was used to analyse the risk factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreaticoduodenectomy, and receiver operating characteristic (ROC) curve was used to assess the predictive value of the factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreatic cancer surgery. Results:Multifactorial analysis showed that the combination of diabetes mellitus ( OR=1.216, 95% CI: 1.225-1.657, P=0.035), occurrence of postoperative pancreatic fistula ( OR=1.292, 95% CI: 1.095-1.829, P=0.022), occurrence of postoperative biliary fistula ( OR=1.239, 95% CI: 1.171-2.618, P=0.005), high D-dimer ( OR=137.030, 95% CI: 3.214-5 843.128, P=0.010), prolonged operation time ( OR=1.058, 95% CI: 1.002-1.117, P=0.042), and low serum albumin ( OR=0.711, 95% CI: 0.508-0.995, P=0.046) in pancreatic cancer patients with a high risk of postoperative venous thromboembolism with the development of abdominal infection. ROC curve analysis showed that diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, duration of surgery, serum albumin, and co-prediction had certain predictive value for the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery, in which, the area under the curve for co-prediction in assessing the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery was 0.993 (95% CI: 0.982-1.000), with a sensitivity of 1.000 and a specificity of 0.929. Conclusion:Comorbid diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, prolonged duration of surgery, and serum albumin are independent risk factors for the development of postoperative venous thromboembolism with abdominal infection in patients with pancreatic cancer, all of which have diagnostic value and a high joint predictive value. The predictive value of co-prediction is relatively high.

Objective To compare the consistency of voice parameters collected by commonly used smart-phone brands in China and professional recording equipment,and to study whether smartphones can be used for voice research.Methods A total of 67 normal subjects were selected for voice recording using six different smart-phone brands(via the"Active Health"screening APP from the National Key Research and Development Program)and professional recording equipment.Acoustic voice parameters such as fundamental frequency parameters,fre-quency variation parameters,amplitude variation parameters,formant parameters,and energy parameters were ex-tracted from the vowels/a/,/i/,and/u/.A one-way ANOVA test and Tukey's HSD post-hoc comparisons were conducted on the independent variables.Results There were no significant differences between smartphones and professional recording equipment in terms of fundamental frequency parameters such as median F0,mean F0,max F0 and min F0;frequency parameters such as jitter local,jitter local absolute,jitter rap,jitter ppq5,and jitter ddp;amplitude parameters such as shimmer local,shimmer local dB,shimmer apq3,shimmer apq5,shimmer apq11,and shimmer dda;and formant parameters such as F1,F2,F3,and F4.However,significant differences were found in energy parameters such as mean energy(F=31.171,P＜0.001),max energy(F=34.193,P＜0.001),and min energy(F=5.453,P＜0.001)between smartphones and professional recording equipment.Conclusion The smartphones using the"Active Health"screening app from the National Key Research and Development Program can replace professional recording equipment for voice research.However,caution should be exercised when selec-ting energy-related acoustic parameters.

Objective:To explore the prevalence and risk factors of insomnia in Chinese Air Force servicemen deployed to highland areas.Methods:A total of 718 Air Force servicemen deployed to Qinghai-Tibetan plateau were recruited at May 2024.Sleep quality was assessed with the Pittsburgh Sleep Quality Index.Social-demograph-ic,military service,and psychological characteristics were measured with a self-administered general question-naire.Bivariable and multivariable logistic regressions were performed to identify independent risk factors.Missing data were handled by the multiple imputation.Results:The average sleep duration was(6.9±1.2)h and the aver-age PSQI score was(5.9±4.1).Totally 53.8％of participants experienced clinically significant insomnia.The multivariable analysis revealed that age≥35(aOR=4.07,95％CI=1.11-17.76),stressful event(aOR=3.27,95％CI=2.00-5.49),dysfunctional sleep beliefs and attitudes(aOR=2.59,95％CI=1.75-3.85),and caffeine product usage(aOR=1.69,95％CI=1.17-2.43)were risk factors for insomnia,while Tibetan-indigenous ethnic(aOR=0.44,95％CI=0.20-0.91),higher perceived social support(aOR=0.96,95％CI=0.96-0.99),and positive coping style(aOR=0.96,95％CI=0.93-0.99)were protective factors.Conclusion:Air force service-men deployed to highland areas have sufficient sleep time,but reduced sleep quality.Age,exposed to stress event during deployment,dysfunctional sleep beliefs and attitudes,and caffeine product usage are risk factors for insomni-a,while Tibetan-indigenous ethnic,higher perceived social support and positive coping style act as protective fac-tors.

ObjectiveTo assess the efficiency of a Sophora flavescens Ait (S. flavescens, Ku Shen)-soluble microneedle (SFA-MN) for improving skin lesion symptoms in mice with psoriasis.MethodsSFA-MNs were prepared using a two-mold molding process with 20% w/v polyvinylpyrrolidone and 15% w/v polyvinyl alcohol. The SFA-MNs were assessed for morphology, mechanical properties, in vitro dissolution, identification of components, and skin lesion improvement in imiquimod-induced psoriasis mice.ResultsThe SFA-MNs demonstrated good mechanical properties for efficiently penetrating the dermis, facilitating efficient drug delivery. Furthermore, they effectively inhibited mast cell levels in the dorsal lesion area of psoriasis mice and reduced the expression of the T-lymphocyte factor cluster of differentiation 3 and tumor necrosis factor-α. In addition, this system alleviated skin inflammation, splenic swelling, and thymic atrophy in the psoriasis-like mouse model. Seven major components were detected from SFA-MNs by comparison of the mass-to-nucleus ratios (m/z) of the secondary fragments N-methylcytisine, 5α, 9α-dihydroxymatrine, sophoramine, matrine, oxysophocarpine, oxymatrine, and kushenol O.ConclusionThe drug delivery strategy combining traditional herbal S. flavescens with soluble microneedle technology provides more targeted and effective immune regulation for treating psoriasis-like mice models, enabling enhanced therapeutic effects compared with the control group.

OBJECTIVE To investigate the inhibitory effect and mechanism of the active components of Alpinia katsumadai （ACAK） on tumor xenograft growth and tumor angiogenesis of human pancreatic cancer PANC-1 cells in nude mice. METHODS A tumor xenograft model in nude mice was established using human pancreatic cancer PANC-1 cells. The mice were randomly divided into model control group （intragastric administration of 0.9% normal saline）， solvent control group （intragastric administration of 0.5% carboxymethyl cellulose sodium）， positive control group （intraperitoneal injection of 0.5% carboxymethyl cellulose sodium+bevacizumab suspension 5 mg/kg ）， and ACAK 50， 100， and 200 mg/kg groups （intragastric administration of 0.5% carboxymethyl cellulose sodium+ACAK suspension 50， 100， 200 mg/kg）. The administration was carried out for 5 consecutive days followed by a 2-day interval， and this cycle was repeated for a total duration of 28 days. The tumor volume （TV）， relative tumor volume （RTV）， and relative tumor proliferation rate （T/C） at various time points from day 1 to day 28 after drug administration were measured and calculated for each group of nude mice. After the drug administration， the tumor weights were measured， and microvessel density （MVD） in the tumor xenograft tissues of nude mice， as well as relative protein expression levels of vascular endothelial growth factor （VEGF） and its receptor [fas-like tyrosine kinase-1 （Flt-1）， kinase insert domain receptor （KDR）] were detected. RESULTS On the 24th day of ACAK administration，compared with the model control group， the TV and RTV （except for ACAK 50 and 100 mg/kg groups） of nude mice in the positive control group and ACAK dose groups were significantly decreased （P＜0.05 or P＜0.01）， and the T/C of ACAK dose groups showed a dose-dependent decrease； the microvascular distribution of nude mice in the positive control group and ACAK dose groups was relatively sparse， and the tumor weight （except for the ACAK 50 mg/kg group）， MVD， and relative expression levels of VEGF， KDR， and Flt-1 in the tumor xenograft tissues were significantly reduced （P＜0.05 or P＜0.01）. CONCLUSIONS ACAK has a good anti-pancreatic cancer effect， and its mechanism may be related to its inhibition of VEGF/ VEGFR signaling pathway， thereby inhibiting angiogenesis in pancreatic cancer.

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

Objective To observe the value of cervical lymph node ultrasound network model based on deep learning(DL)for diagnosing cervical lymph node metastasis of papillary thyroid carcinoma(PTC).Methods Totally 444 PTC patients with suspected cervical lymph node enlargement on ultrasonography were retrospectively enrolled and divided into metastasis group(n=253)and non-metastasis group(n=191)based on fine needle aspiration pathology and thyroglobulin detection.And 1 754 cervical lymph node ultrasonic images were divided into training set(n=1 404),validation set(n=175)and test set(n=175)at the ratio of 8∶1∶1.Ultrasonic features of cervical lymph nodes were extracted,then ultrasound network model was established using DL,and the diagnostic efficacy of this model for diagnosing lymph node metastasis was analyzed.Results The sensitivity,specificity,accuracy and area under the curve of ultrasound network model for diagnosing cervical lymph node metastasis of PTC was 93.06％,97.05％,95.04％and 0.858 in validation set,which was 86.14％,78.67％,88.49％and 0.828 in test set,respectively.Conclusion Cervical lymph node ultrasound network model based on DL was helpful for diagnosing cervical lymph node metastasis of PTC.